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surfing, advanced search, search tools
Chemistry, mathematics, physical sciences, engineering, aerospace, astronomy,
photography, news, computers, software, investment, venture capital,
stakeholder, Biology, Chemistry, Geosciences, Biotechnology, Medical, Nursing,
Anthropology, psychology, psychiatry, Philosophy, History, Business, bachelor,
Ph.D., Masters, administrative, MBA, eigenMethod, eigenvector, mathematical,
manifolds, science, technical, search tools, eigenmath, Jacobian, quantum,
mechanics, manifolds, physics, chemistry, law, legal, health, government, home,
office, business, domain, lookup, medical, travel, food, university, students,
search, searches, search engine, directory, directories, category, categories,
help, searching, searchers, surfing, advanced search, search help, search tips
Beta Users and advanced features Sign-up here...
http://www.eigensearch.com/inc/constructs/betasignup.htm
Central to eigenSearch Advanced is the freedom to construct complex search
explorations, save the forms for later use; and apply weight factor to each
phrase and term. EigenSearch processing will apply eigenvector math and Jacobian
matrices to construct search terms that are tailored to your exploration.
Cross-pollination is also applied as described below. The eigenvector approach
is clearly highly advanced and would normally be useful for very sophisticated
applications. Nevertheless, anyone may utilize the method. An advanced form is
simply a matrix in which the user types words and phrases randomly in a
multi-cell form (please click thumbnail to view).
Advanced features
EigenOperator (cross pollination) and eigenvector constructs
Cross document content pollination within every web site directory tree (unlike
conventional search engines and tools eigenSearch checks for your terms and
phrases and drills down though multiple directory documents)
EigenSearch cross-pollination is applied to documents within the same (tree)
level in a URL (peer documents). Thereby limiting the amount of contamination of
results
Illustration:
"Blood Hounds" + "English Breed" will present documents that contain either of
these phrases within the same peer level in a document storage structure; for
example, within the directory: www.smartdogs/hounds.
eigenSearch limits pollinating occurrences outside a peer level. For example;
"blood hounds" + "English breed" found in two different directories would not
report an eigenSearch result: i.e. "Blood hounds" found in www.smartdogs/hounds
and "English Breed" found in. www.smartdogs/hounds/Europe would not be found.
EigenSearch therefore searches one tree (peer) level in a site and looks for
multiple occurrences of multiple phrases across all documents within this peer
level.
Corporate products can be tailored to drill down infinite levels for
eigenOperator (cross-pollinating operator) matching.
eigenSearch single phrase results will find all documents and show the results
as independent findings. This way the user can find results across many
documents and the combined highly constrained results are reserved for a single
level cross pollination.
Extremely high (cross-pollinating) eigenValues will correspond to finely
granular and refined search explorations.
Beta users receive the following features:
Login and password
Save search constructs for later use in your own personal construct tables
EigenOperator (cross Pollinating Operator) advanced features as described above
(eigenvector to follow)
Database (Table) upload and eigenvector computations
EigenSearch seeks 300,000 beta testers for its advanced eigenOperator based
cognitive engine. This engine will allow for a multiplicity of search parameters
for users to select so as to mathematically narrow results. The system will
employ eigenVectors, eigenValues and eigenMatrices to determine relevance to
user searches; thereby rendering high fidelity confirmed search results.
Naturally the computational power for doing such math is why beta testers are
required. Each tester is welcome to comment on user friendliness, speed, change
and ergonomic elegance. It is an eigenSearch goal to continue advancing the user
interface so as to remain intuitively simple to use while at the same time
providing hi-fidelity explorations.
All beta testers will receive a login and password, which provides entry into
features for saving search constructs and parameters according to their own
classification approach. Saved results and parameters can be used at any time
and modified to alter search results. Beta users will be able to import their
own data sets (2-dimentional) and perform an eigenValue analysis.
<r<p_

What a hoot!
If this confuses you, perhaps this will help..
"Eigen Vector
An n*n matrix is a matrix with n rows and n columns.
An n*n matrix is a linear transformation of n-dimensional Euclidean space. An
EigenVector of an n*n matrix is a vector that gives a direction in which that
transformation is simply a scaling. The amount of scaling is the associated
EigenValue. This is incredibly valuable because it lets you do
MatrixFactoring and MatrixAnalysis.
See also http://foldoc.doc.ic.ac.uk/foldoc/foldoc.cgi?eigenvector ."

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